Sajjad Jahanbakhsh Gudakahriz, Amir Masoud Eftekhari Moghadam, Fariborz Mahmoudi
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Opinion texts summarization based on texts concepts with multi-objective pruning approach.
Considering the huge volume of opinion texts published on various social networks, it is extremely difficult to peruse and use these texts. The automatic creation of summaries can be a significant help for the users of such texts. The current paper employs manifold learning to mitigate the challenges of the complexity and high dimensionality of opinion texts and the K-Means algorithm for clustering. Furthermore, summarization based on the concepts of the texts can improve the performance of the summarization system. The proposed method is unsupervised extractive, and summarization is performed based on the concepts of the texts using the multi-objective pruning approach. The main parameters utilized to perform multi-objective pruning include relevancy, redundancy, and coverage. The simulation results show that the proposed method outperformed the MOOTweetSumm method while providing an improvement of 11% in terms of the ROGUE-1 measure and an improvement of 9% in terms of the ROGUE-L measure.
期刊介绍：
The Journal of Supercomputing publishes papers on the technology, architecture and systems, algorithms, languages and programs, performance measures and methods, and applications of all aspects of Supercomputing. Tutorial and survey papers are intended for workers and students in the fields associated with and employing advanced computer systems. The journal also publishes letters to the editor, especially in areas relating to policy, succinct statements of paradoxes, intuitively puzzling results, partial results and real needs.
Published theoretical and practical papers are advanced, in-depth treatments describing new developments and new ideas. Each includes an introduction summarizing prior, directly pertinent work that is useful for the reader to understand, in order to appreciate the advances being described.
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